Decision Trees vs Random Forests, Explained by Natassha Selvaraj via @kdnuggets

A simple, non-math-heavy explanation of two popular tree-based machine learning models.

I liked this which was nice and easy to understand if you aren't great at maths and formulas.

Decision Tree Algorithm, Explained by Nagesh Singh Chauhan via @kdnuggets

All you need to know about decision trees and how to build and optimize decision tree classifiers.

A very clear and easy to understand guide that you might want to share with any folks that need the detailed information in it.

How the random forest algorithm works in machine learning by @saimadhup via @dataaspirant

This is a great article by Saimadhu Polamuri which is a good explanation of how Random Forest works.

Definitely work reading. Contains some great diagrams.

WEBINAR: Webinar: Improve Your Regression with CART and Gradient Boosting - 16 February 2017

Improve Your Regression with CART and Gradient Boosting

Join us for our upcoming webinar:

Date: Thursday, February 16, 2017

Time: 1 pm EST, 10 am PST

Can't make it at this time? Register to receive a recorded copy of the webcast and presentation slides, which we will email out a few days after the live event.

Duration: 55 minutes

Speaker: Charles Harrison, Marketing Statistician, Salford Systems

Cost: Free 

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Abstract: In this webinar we'll introduce you to a powerful tree-based machine learning algorithm called gradient boosting. Gradient boosting often outperforms linear regression, Random Forests, and CART. Boosted trees automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values.

We'll see that CART decision trees are the foundation of gradient boosting and discuss some of the advantages of boosting versus a Random Forest. We will explore the gradient boosting algorithm and discuss the most important modeling parameters like the learning rate, number of terminal nodes, number of trees, loss functions, and more. We will demonstrate using an implementation of gradient boosting (TreeNet® Software) to fit the model and compare the performance to a linear regression model, a CART tree, and a Random Forest.

Register here